Nurse practitioners: Developing the next generation of health care leaders through an internship program
Bibliographic record
Abstract
The current health care environment demands competent providers who possess advanced knowledge and skills necessary to care for an increasingly acute patient population. Nurse Practitioners (NPs) are uniquely positioned to meet the challenges of today’s health care climate because they blend their ability to treat illness with a strong emphasis on patient safety and outcomes while providing patient- and family-centered care. The role of the NP requires sophisticated clinical and critical thinking skills that foster the ability to synthesize theoretical, scientific, and contemporary clinical knowledge for the assessment and management of both healthy and illness states. It is crucial that NPs possess advanced skills to provide safe and effective patient care outcomes. The literature suggests that there is a transition period after graduation for novice NPs. Developing additional focused educational opportunities to supplement the training phase of their graduate education can better prepare student NPs and consequently enhance this transition phase. As a result, newly licensed NPs can deliver high-quality, safe patient care sooner after obtaining their advanced practice license. At a time when health care reform is rapidly evolving, it is critical that NPs are reliable, competent professionals. Houston Methodist Hospital created an employment-based educational opportunity for elite graduate NP students: Leading Education and Advancing Professionals (LEAP). This 12-month program is a structured evidence-based internship program that incorporates didactic training, clinical rotations, and simulation. Students are provided hands-on training that features interdisciplinary and collaborative care model components from experienced Houston Methodist NPs. The goal is to help develop the next generation of health care providers in which the expectation is to lead and not follow in meeting today’s health care challenges. The aim of this article is to present an overview of the LEAP program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".